scientific article; zbMATH DE number 7164710
From MaRDI portal
Publication:5214198
zbMath1434.68526arXiv1802.09210MaRDI QIDQ5214198
Publication date: 7 February 2020
Full work available at URL: https://arxiv.org/abs/1802.09210
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Artificial neural networks and deep learning (68T07) Neural nets and related approaches to inference from stochastic processes (62M45)
Related Items (21)
Sparse Deep Neural Network for Nonlinear Partial Differential Equations ⋮ Deep learning for the partially linear Cox model ⋮ What Kinds of Functions Do Deep Neural Networks Learn? Insights from Variational Spline Theory ⋮ Convex optimization in sums of Banach spaces ⋮ Approximation of Lipschitz Functions Using Deep Spline Neural Networks ⋮ Unnamed Item ⋮ Linear inverse problems with Hessian-Schatten total variation ⋮ On the number of regions of piecewise linear neural networks ⋮ Sparse regularization with the ℓ0 norm ⋮ Unnamed Item ⋮ TV-based reconstruction of periodic functions ⋮ Deep Learning for Trivial Inverse Problems ⋮ Mini-workshop: Deep learning and inverse problems. Abstracts from the mini-workshop held March 4--10, 2018 ⋮ A unifying representer theorem for inverse problems and machine learning ⋮ Unnamed Item ⋮ Sparsest piecewise-linear regression of one-dimensional data ⋮ The Gap between Theory and Practice in Function Approximation with Deep Neural Networks ⋮ Stable interpolation with exponential-polynomial splines and node selection via greedy algorithms ⋮ CAS4DL: Christoffel adaptive sampling for function approximation via deep learning ⋮ Deep learning architectures for nonlinear operator functions and nonlinear inverse problems ⋮ A hybrid stochastic optimization framework for composite nonconvex optimization
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A mathematical introduction to compressive sensing
- Interpolation of scattered data: distance matrices and conditionally positive definite functions
- Kernel methods in machine learning
- Spline solutions to L\(^1\) extremal problems in one and several variables
- A practical guide to splines
- Locally adaptive regression splines
- Region configurations for realizability of lattice piecewise-linear models.
- Regularization networks and support vector machines
- Some results on Tchebycheffian spline functions and stochastic processes
- Kernels for Vector-Valued Functions: A Review
- Representer Theorems for Sparsity-Promoting <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math> </inline-formula> Regularization
- Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks
- Generalization of Hinging Hyperplanes
- Splines Are Universal Solutions of Linear Inverse Problems with Generalized TV Regularization
- Continuous-Domain Solutions of Linear Inverse Problems With Tikhonov Versus Generalized TV Regularization
- Learning representations by back-propagating errors
- A representer theorem for deep kernel learning
- For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution
- SPLINE FUNCTIONS AND THE PROBLEM OF GRADUATION
- Theory of Reproducing Kernels
This page was built for publication: